Material jetting high quality components via an inverse problem framework

Material jetting is a key additive manufacturing technology to enable the production of microscale components, but its application is limited by susceptibility to deviations from intended design and performance. The main challenge remains in producing accurate and functional parts in a reproducible and timely manner, particularly for fine featured geometries. Existing frameworks to mitigate part deviations are based on computationally expensive physics-based simulations, but little attention has been paid to techniques suitable for high-throughput conditions. Here we develop and validate an inverse problem framework which facilitates predicting the morphology of printed features with a Shape from Shading (SFS) photometric technique coupled with the rapid optimisation of process parameters to enhance the quality of the product. The framework incorporates the critical printing parameters including drop spacing, printing frequency and standoff distance such that we are able to build the morphology of complex patterns with an error percentage less than 10 % in seconds. The inverse problem framework is highly versatile since it can be utilized in conjunction with computer vision algorithms for accurate in-situ inspection of printed features and to find the optimal printing parameters for freeform patterns considering different materials and substrates or even other AM technologies to improve the quality of printed parts in a timely manner.